





'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class __a			:





   def __init__(       self					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Dict=13					,lowerCamelCase					:      Dict=7					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Tuple=True					,lowerCamelCase					:      List[Any]=False					,lowerCamelCase					:      List[str]=True					,lowerCamelCase					:      List[str]=99					,lowerCamelCase					:      Optional[int]=32					,lowerCamelCase					:      Any=5					,lowerCamelCase					:      Any=4					,lowerCamelCase					:      Union[str, Any]=37					,lowerCamelCase					:      Optional[int]="gelu"					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      Union[str, Any]=0.1					,lowerCamelCase					:      Optional[int]=512					,lowerCamelCase					:      Dict=16					,lowerCamelCase					:      Dict=2					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      Optional[int]=3					,lowerCamelCase					:      str=4					,lowerCamelCase					:      Tuple=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							seq_length
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_input_mask
      __SCREAMING_SNAKE_CASE   				=							use_token_type_ids
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							type_vocab_size
      __SCREAMING_SNAKE_CASE   				=							type_sequence_label_size
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							num_choices
      __SCREAMING_SNAKE_CASE   				=							scope





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_input_mask:
         __SCREAMING_SNAKE_CASE   				=							random_attention_mask([self.batch_size, self.seq_length]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_token_type_ids:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.type_vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.type_sequence_label_size     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.num_labels     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_choices     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return LlamaConfig(
          vocab_size=self.vocab_size					,hidden_size=self.hidden_size					,num_hidden_layers=self.num_hidden_layers					,num_attention_heads=self.num_attention_heads					,intermediate_size=self.intermediate_size					,hidden_act=self.hidden_act					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,max_position_embeddings=self.max_position_embeddings					,type_vocab_size=self.type_vocab_size					,is_decoder=lowerCamelCase					,initializer_range=self.initializer_range					,)





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[Any]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      # first forward pass
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,use_cache=lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((self.batch_size, 3)					,config.vocab_size     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((self.batch_size, 3)					,vocab_size=2     )

      # append to next input_ids and
      __SCREAMING_SNAKE_CASE   				=							torch.cat([input_ids, next_tokens]					,dim=-1     )
      __SCREAMING_SNAKE_CASE   				=							torch.cat([input_mask, next_mask]					,dim=-1     )

      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,output_hidden_states=lowerCamelCase					,)["""hidden_states"""][0]
      __SCREAMING_SNAKE_CASE   				=							model(
          lowerCamelCase					,attention_mask=lowerCamelCase					,encoder_hidden_states=lowerCamelCase					,encoder_attention_mask=lowerCamelCase					,past_key_values=lowerCamelCase					,output_hidden_states=lowerCamelCase					,)["""hidden_states"""][0]

      # select random slice
      __SCREAMING_SNAKE_CASE   				=							ids_tensor((1,)					,output_from_past.shape[-1]     ).item()
      __SCREAMING_SNAKE_CASE   				=							output_from_no_past[:, -3:, random_slice_idx].detach()
      __SCREAMING_SNAKE_CASE   				=							output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-3     )     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  
      )   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			Optional[Any]						   =						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
   __UpperCamelCase :			int						   =						(LlamaForCausalLM,) if is_torch_available() else ()
   __UpperCamelCase :			Union[str, Any]						   =						(
       {
           'feature-extraction': LlamaModel,
           'text-classification': LlamaForSequenceClassification,
           'text-generation': LlamaForCausalLM,
           'zero-shot': LlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCamelCase :			List[str]						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LlamaModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,hidden_size=37     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __SCREAMING_SNAKE_CASE   				=							type
         self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.model_tester.batch_size]					,self.model_tester.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.assertEqual(result.logits.shape					,(self.model_tester.batch_size, self.model_tester.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							"""single_label_classification"""
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.model_tester.batch_size]					,self.model_tester.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.assertEqual(result.logits.shape					,(self.model_tester.batch_size, self.model_tester.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							3
      __SCREAMING_SNAKE_CASE   				=							"""multi_label_classification"""
      __SCREAMING_SNAKE_CASE   				=							input_dict["""input_ids"""]
      __SCREAMING_SNAKE_CASE   				=							input_ids.ne(1     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					,self.model_tester.type_sequence_label_size     ).to(torch.float     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,attention_mask=lowerCamelCase					,labels=lowerCamelCase     )
      self.assertEqual(result.logits.shape					,(self.model_tester.batch_size, self.model_tester.num_labels)     )





   @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""     )
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      pass





   @parameterized.expand([("""linear""",), ("""dynamic""",)]     )
   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([1, 10]					,config.vocab_size     )
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([1, int(config.max_position_embeddings * 1.5     )]					,config.vocab_size     )

      set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      original_model.to(lowerCamelCase     )
      original_model.eval()
      __SCREAMING_SNAKE_CASE   				=							original_model(lowerCamelCase     ).last_hidden_state
      __SCREAMING_SNAKE_CASE   				=							original_model(lowerCamelCase     ).last_hidden_state

      set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
      __SCREAMING_SNAKE_CASE   				=							{"""type""": scaling_type, """factor""": 10.0}
      __SCREAMING_SNAKE_CASE   				=							LlamaModel(lowerCamelCase     )
      scaled_model.to(lowerCamelCase     )
      scaled_model.eval()
      __SCREAMING_SNAKE_CASE   				=							scaled_model(lowerCamelCase     ).last_hidden_state
      __SCREAMING_SNAKE_CASE   				=							scaled_model(lowerCamelCase     ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
         self.assertTrue(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-5     )     )
      else:
         self.assertFalse(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-5     )     )

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(lowerCamelCase					,lowerCamelCase					,atol=1E-5     )     )







@require_torch
class __a			( unittest.TestCase      ):





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor([input_ids]     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]     )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]					,lowerCamelCase					,atol=1E-5					,rtol=1E-5     )





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]     )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]					,lowerCamelCase					,atol=1E-5					,rtol=1E-5     )





   @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )
      # Expected mean on dim = -1
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]     )
      # fmt: on
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )





   @unittest.skip(
       """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[1, 306, 4658, 278, 6593, 310, 2834, 338]
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""					,device_map="""auto"""     )
      __SCREAMING_SNAKE_CASE   				=							model(torch.tensor(lowerCamelCase     )     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]					,dtype=torch.floataa     )
      torch.testing.assert_close(out.mean(-1     )					,lowerCamelCase					,atol=1E-2					,rtol=1E-2     )
      # fmt: off
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]     )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]					,lowerCamelCase					,atol=1E-5					,rtol=1E-5     )





   @unittest.skip("""Model is curently gated"""     )
   @slow
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
      __SCREAMING_SNAKE_CASE   				=							"""Simply put, the theory of relativity states that """
      __SCREAMING_SNAKE_CASE   				=							LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.encode(lowerCamelCase					,return_tensors="""pt"""     )
      __SCREAMING_SNAKE_CASE   				=							LlamaForCausalLM.from_pretrained(
          """meta-llama/Llama-2-13b-chat-hf"""					,device_map="""sequential"""					,use_safetensors=lowerCamelCase     )

      # greedy generation outputs
      __SCREAMING_SNAKE_CASE   				=							model.generate(lowerCamelCase					,max_new_tokens=64					,top_p=lowerCamelCase					,temperature=1					,do_sample=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.decode(generated_ids[0]					,skip_special_tokens=lowerCamelCase     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )












"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






